… We present current deep learning works on machine health monitoring in a well-organized
way to facilitate researchers to catch this topic and provide discussions about the future …

… The present work presents the review of the smart health monitoring frameworks which
successfully implemented the deep learning and machine learning approaches and algorithms …

… new issues in machinery health monitoring. To harness the properties of mechanical big
data and the advantages of deep learning theory, a health monitoring and fault diagnosis …

… deep neural networks can be used to detect potential anomalies in large amounts of unchecked
structural health monitoring data… , acceleration data from the structural health monitoring …

… novel convolutional neural network (CNN)-based approach for structural health monitoring
(SHM) that exploits a form of measured compressed response data through transfer learning (…

… amount of monitoring data by the use of machine learning methods, … monitoring data will
not only help us accurately and A review on deep learning-based structural health monitoring …

… develops a practical data-driven method for monitoring the … deep learning (DL). The proposed
method operates on processed data where relevant features extracted from sensory data …

… deep learning technique to characterize the damage in the form of cracks on a composite
material. The deep learning … This paper proposes to formulate and apply a deep learning …

… works of deep learning for machine health monitoring. Various … and recurrent neural networks,
restricted Boltzmann machine, and its … the DL-based machine health monitoring approach. …

… from the raw input data through stacked blocks of deep neural network (DNN) layers with …
DNN is responsible for learning a new representation of the input response data and, thus, …